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Watershed Planning within a Quantitative Scenario Analysis Framework
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Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue.

Mohammad Najafzadeh1, Sedigheh Anvari2

  • 1Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, P.O. Box 76315117, Kerman, Iran. moha.najafzadeh@gmail.com.

Environmental Science and Pollution Research International
|June 27, 2023
PubMed
Summary

This study quantifies uncertainty in artificial intelligence (AI) models for streamflow forecasting. Model Tree (MT) demonstrated lower uncertainty compared to Multivariate Adaptive Regression Spline (MARS) and Gene-Expression Programming (GEP).

Keywords:
Artificial intelligence modelsStatistical measuresStreamflow forecastUncertainty analysis

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Area of Science:

  • Hydrology and Water Resources Engineering
  • Artificial Intelligence in Environmental Science
  • Data-driven Modeling for Water Management

Background:

  • Artificial intelligence (AI) techniques like Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) are increasingly used in water resources.
  • There is a lack of research on the uncertainty levels of these AI models, which is critical for reliable streamflow forecasting.
  • Accurate streamflow forecasts are essential to prevent the repercussions of poorly managed water resources.

Purpose of the Study:

  • To investigate and quantify the uncertainty associated with GEP, MT, and MARS models in streamflow forecasting.
  • To compare the uncertainty levels of these three AI techniques using a global daily streamflow dataset.
  • To evaluate the suitability of these models for practical streamflow prediction applications.

Main Methods:

  • Utilized a global daily streamflow dataset for model training and validation.
  • Employed Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) for streamflow forecasting.
  • Quantified model uncertainty using 95% Percent Prediction Uncertainty (95%PPU) and R-factor statistical indicators.

Main Results:

  • Model Tree (MT) exhibited the lowest uncertainty with a 95%PPU of 0.59 and an R-factor of 1.67.
  • Multivariate Adaptive Regression Spline (MARS) showed slightly higher uncertainty (95%PPU=0.61, R-factor=1.92).
  • Gene-Expression Programming (GEP) presented the highest uncertainty among the tested models (95%PPU=0.64, R-factor=2.03).
  • While uncertainty bands generally captured mean streamflow measurements, wide bands indicated significant uncertainty in monthly streamflow predictions.

Conclusions:

  • Model Tree (MT) is a more reliable AI technique for streamflow forecasting due to its lower uncertainty.
  • Despite capturing mean values, the wide uncertainty bands highlight the need for caution when using these AI models for critical water resource management decisions.
  • Further research is needed to reduce the uncertainty in AI-based streamflow forecasting models.